---
sidebar_position: 2
---
import { constants } from '@site/src/constants';
import CodeBlock from '@theme/CodeBlock';

# Jupyter Mode

In addition to executing code as a complete script, the sandbox also supports running a series of code snippets through Jupyter. The main differences in Jupyter mode are:

- When a Cell stops running due to an exception, subsequent Cells will continue to execute
- In addition to standard stdout and stderr outputs, two new output streams are added:
  - display stream, supporting rich text, where the results of the last statement in each Cell and matplotlib-generated charts will be output
  - error stream, where uncaught exceptions will be output

The following Python script demonstrates the usage and features of Jupyter mode:

export const snippet1 = String.raw`import json
import base64
import requests

cells = [
    '''
a = 123
# display stream
a
    ''',
    '''
# stdout & stderr stream
print(a)
import sys
sys.stderr.write(str(a))
    ''',
    '''
raise Exception("This exception doesn't interrupt cell execution")
    ''',
    '''
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 2, 3])
    '''
]

response = requests.post('${ constants.host }/run_jupyter', json={
    'cells': cells,
    'kernel': 'python3'
})

print(json.dumps(response.json(), indent=2))
`

<CodeBlock language="python">
{snippet1}
</CodeBlock>


Output:

```json
{
  "status": "Success",
  "message": "",
  "driver": {
    "status": "Finished",
    "execution_time": 2.6821532249450684,
    "return_code": 0,
    "stdout": "config: \n{\n  \"kernel\": \"python3\",\n  \"cells\": [\n    \"\\na = 123\\n# display stream\\na\\n    \",\n    \"\\n# stdout & stderr stream\\nprint(a)\\nimport sys\\nsys.stderr.write(str(a))\\n    \",\n    \"\\nraise Exception(\\\"This exception doesn't interrupt cell execution\\\")\\n    \",\n    \"\\nimport matplotlib.pyplot as plt\\n\\nplt.plot([1, 2, 3], [1, 2, 3])\\n    \"\n  ],\n  \"cell_timeout\": 0.0,\n  \"total_timeout\": 45.0\n}\n",
    "stderr": ""
  },
  "cells": [
    {
      "stdout": "",
      "stderr": "",
      "display": [
        {
          "text/plain": "123"
        }
      ],
      "error": []
    },
    {
      "stdout": "123\n",
      "stderr": "123",
      "display": [
        {
          "text/plain": "3"
        }
      ],
      "error": []
    },
    {
      "stdout": "",
      "stderr": "",
      "display": [],
      "error": [
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis exception doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt interrupt cell execution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "\u001b[0;31mException\u001b[0m: This exception doesn't interrupt cell execution"
          ],
          "ename": "Exception",
          "evalue": "This exception doesn't interrupt cell execution"
        }
      ]
    },
    {
      "stdout": "",
      "stderr": "",
      "display": [
        {
          "text/plain": "[<matplotlib.lines.Line2D at 0x7f7d795c1ff0>]"
        },
        {
          "text/plain": "<Figure size 640x480 with 1 Axes>",
          "image/png": 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"
        }
      ],
      "error": []
    }
  ],
  "executor_pod_name": null,
  "files": {}
}
```

---

Q: Why not adopt the approach of creating a Session + executing one Cell per request, instead of executing all Cells each time?

A: To maintain the stateless nature of the sandbox service, reducing maintenance and usage costs. The sandbox service is for offline scenarios, where throughput is more important than latency.